#First we load our data into our environment

library(tidytuesdayR) 

tuesdata <- tidytuesdayR::tt_load('2025-06-24')
## ---- Compiling #TidyTuesday Information for 2025-06-24 ----
## --- There are 2 files available ---
## 
## 
## ── Downloading files ───────────────────────────────────────────────────────────
## 
##   1 of 2: "cases_month.csv"
##   2 of 2: "cases_year.csv"
cases_month <- tuesdata$cases_month
cases_year <- tuesdata$cases_year
#We start by 

library(rayshader)
library(rgl)
library(av)
library(gifski)
library(magick)
## Linking to ImageMagick 6.9.12.98
## Enabled features: cairo, freetype, fftw, ghostscript, heic, lcms, pango, raw, rsvg, webp
## Disabled features: fontconfig, x11
library(ggplot2)
library(dplyr)
## 
## Vedhæfter pakke: 'dplyr'
## De følgende objekter er maskerede fra 'package:stats':
## 
##     filter, lag
## De følgende objekter er maskerede fra 'package:base':
## 
##     intersect, setdiff, setequal, union
library(magrittr)
library(plotly)
## 
## Vedhæfter pakke: 'plotly'
## Det følgende objekt er maskeret fra 'package:ggplot2':
## 
##     last_plot
## Det følgende objekt er maskeret fra 'package:stats':
## 
##     filter
## Det følgende objekt er maskeret fra 'package:graphics':
## 
##     layout
names(cases_month)
##  [1] "region"                "country"               "iso3"                 
##  [4] "year"                  "month"                 "measles_suspect"      
##  [7] "measles_clinical"      "measles_epi_linked"    "measles_lab_confirmed"
## [10] "measles_total"         "rubella_clinical"      "rubella_epi_linked"   
## [13] "rubella_lab_confirmed" "rubella_total"         "discarded"
names(cases_year)
##  [1] "region"                                                         
##  [2] "country"                                                        
##  [3] "iso3"                                                           
##  [4] "year"                                                           
##  [5] "total_population"                                               
##  [6] "annualized_population_most_recent_year_only"                    
##  [7] "total_suspected_measles_rubella_cases"                          
##  [8] "measles_total"                                                  
##  [9] "measles_lab_confirmed"                                          
## [10] "measles_epi_linked"                                             
## [11] "measles_clinical"                                               
## [12] "measles_incidence_rate_per_1000000_total_population"            
## [13] "rubella_total"                                                  
## [14] "rubella_lab_confirmed"                                          
## [15] "rubella_epi_linked"                                             
## [16] "rubella_clinical"                                               
## [17] "rubella_incidence_rate_per_1000000_total_population"            
## [18] "discarded_cases"                                                
## [19] "discarded_non_measles_rubella_cases_per_100000_total_population"
cases_year <- cases_year %>% 
  mutate(region = recode(region, `AFRO` = "African Region", `AMRO` = "Regon of Americas", `EMRO` = "Eastern Mediterraenean Region", `EURO` = "European Region", `SEARO` = "Sout-East Asian Region", `WPRO` = "Western Pacific Region"))
cases_year %>%
  group_by(year) %>% 
  mutate(avg_reg = mean(measles_incidence_rate_per_1000000_total_population))
## # A tibble: 2,382 × 20
## # Groups:   year [14]
##    region         country iso3   year total_population annualized_population_m…¹
##    <chr>          <chr>   <chr> <dbl>            <dbl>                     <dbl>
##  1 African Region Algeria DZA    2012         37646166                  37646166
##  2 African Region Algeria DZA    2013         38414172                  38414172
##  3 African Region Algeria DZA    2014         39205031                  39205031
##  4 African Region Algeria DZA    2015         40019529                  40019529
##  5 African Region Algeria DZA    2016         40850721                  40850721
##  6 African Region Algeria DZA    2017         41689299                  41689299
##  7 African Region Algeria DZA    2018         42505035                  42505035
##  8 African Region Algeria DZA    2019         43294546                  43294546
##  9 African Region Algeria DZA    2020         44042091                  44042091
## 10 African Region Algeria DZA    2021         44761099                  44761099
## # ℹ 2,372 more rows
## # ℹ abbreviated name: ¹​annualized_population_most_recent_year_only
## # ℹ 14 more variables: total_suspected_measles_rubella_cases <dbl>,
## #   measles_total <dbl>, measles_lab_confirmed <dbl>, measles_epi_linked <dbl>,
## #   measles_clinical <dbl>,
## #   measles_incidence_rate_per_1000000_total_population <dbl>,
## #   rubella_total <dbl>, rubella_lab_confirmed <dbl>, …
cases_year %>%
  group_by(year) %>% 
  mutate(avg_reg = mean(measles_incidence_rate_per_1000000_total_population)) %>% 
ggplot(aes(x = year, y = avg_reg)) + 
  geom_point() +
  theme_bw() +
  labs(title = "Avg. measles incidence rate worldwide \n(pr. 1,000,000)", x = "Year", y = "Avg. Incidence Rate") +
  geom_smooth()
## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'

plotfacet <- cases_year %>%
  group_by(year, region) %>% 
  mutate(avg_reg = mean(measles_incidence_rate_per_1000000_total_population)) %>% 
ggplot() + 
  geom_point(aes(x = year, y = avg_reg, colour = region, shape = region)) +
  theme_bw() +
  labs(title = "Avg. measles incidence rate pr. region pr. year(pr. 1,000,000)", x = "Year", y = "Avg. Incidence Rate", color = "Region", shape = "Region") +
  geom_smooth(aes(x = year, y = avg_reg), color = "grey", se=FALSE, show.legend = FALSE) + 
  facet_wrap(~ region, scales = "free") +
  geom_line(aes(x = year, y = avg_reg, colour = region, shape = region))
## Warning in geom_line(aes(x = year, y = avg_reg, colour = region, shape =
## region)): Ignoring unknown aesthetics: shape
ggplotly(plotfacet)
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
cases_year1 <- cases_year %>% 
  group_by(year, region) %>% 
  mutate(avg_reg = mean(measles_incidence_rate_per_1000000_total_population)) 

hexgg <-  ggplot(cases_year1, aes(x =  year, y = avg_reg)) +
    stat_bin_hex(aes(fill = after_stat(density), colour = after_stat(density)), 
                 bins = 10,
                 linewidth = 1) +
    scale_fill_viridis_c(option = "B") +
    scale_color_viridis_c(option = "B", guide = "none") +
    labs(x = "Year", y = "Avg. incidence rate pr. region", fill = "",
         colour = "") +
    theme_minimal()
hexgg

plot_gg(hexgg, multicore = TRUE, windowsize = c(800, 800))

render_movie("silly.gif")
## [1] "C:\\Users\\au483794\\OneDrive - Aarhus universitet\\Documents\\Fridayproject\\Peter\\silly.gif"
cases_month %>%
  mutate(region = as.factor(region)) %>% 
  ggplot(aes(x = measles_suspect, y = measles_lab_confirmed, color = region)) +
  geom_point() + 
  facet_wrap(~region) + 
  theme_bw()
## Warning: Removed 148 rows containing missing values or values outside the scale range
## (`geom_point()`).